UNI S6: K medoids, Gaussian naive bayes & dbscan on SORLIE dataset

BPNN, K-means, K-medoids

Gaussian mixture models, k-means, mini-batch-kmeans and k-medoids clustering

analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.

Customer Segmentation

Implemented K-Means and K-Medoids on custom dataset. ML ASSIGNMENT 3 => Q3

A small repository implementing clustering algorithm from scratch

Data analysis of marketing campaigns

Algorithms for K-Medoids Clustering

[ECML 2022] SECLEDS: Sequence Clustering in Evolving Data Streams via Multiple Medoids and Medoid Voting

Explore insightful projects on data analysis with MATLAB: k-means, k-medoid, and LDA. Polished PDF reports generated using LaTeX showcase valuable insights from diverse datasets. Discover the power of numerical methods in extracting knowledge from data!

The Partitioning Around Medoids (PAM) implementation of the K-Medoids algorithm in Python [Unmaintained]

Machine Learning Algorithm Implementation from Scratch using Pyhon

Unsupervised machine learning methods built from scratch, KMeans, KMedoids

TCC do curso de pós graduação em Ciência de Dados da PUC-MG (oferta 2021)

Prototype based clustering on seeds dataset

Using the credit card customer base dataset, identify different segments in the existing customer base, taking into account their spending patterns as well as past interactions with the bank.

Unsupervised machine learning on type of glass dataset

Performed clustering analysis on OnSports player data for the English Premier League. The clustering analysis successfully identified 4 unique player clusters and uncovered valuable business recommendations by identifying trends and patterns in the EDA, meeting the objective of determining player pricing next season.